Enhance your PharmaSUG experience by attending optional post-conference training seminars taught by seasoned experts. Half-day courses are only $125 with a conference registration, or $250 without a conference registration. Three seminars will be offered on June 1-4, which is the week following the PharmaSUG conference, and the remainder will be scheduled over the rest of 2021, with the complete schedule to be announced soon. If you are registering or have registered for the conference, you can sign up for these three now and any others throughout the year using the PharmaSUG conference registration system, and take advantage of the discounted price for all of them. However, you must register for the conference first in order to receive the discount.

If you wish to register for the seminars ONLY, without attending the conference, please use this link instead.

Tuesday, June 1, 2021

Course Title (click for description) Instructor(s) (click for bio) Time (U.S. Eastern time zone)
#11 FDA & PMDA Submission Data Requirements Dave Izard 10:00 AM - 2:30 PM

Thursday, June 3, 2021

Course Title (click for description) Instructor(s) (click for bio) Time (U.S. Eastern time zone)
#12 Use Cases for Value-Level Metadata Sandra Minjoe
& Mario Widel
10:00 AM - 2:30 PM

Friday, June 4, 2021

Course Title (click for description) Instructor(s) (click for bio) Time (U.S. Eastern time zone)
#13 Python Programing Seminar – Basic for Statistical Programmers and Statisticians Kevin Lee Noon - 4:30 PM

Course Descriptions

FDA & PMDA Submission Data Requirements
Dave Izard
Tuesday, June 1, 2021, 10:00 AM - 2:30 PM ET

The binding guidance documents requiring you to provide data and related documentation based on US FDA endorsed data standards as part of your electronic submission are in effect for both clinical and non-clinical assets. These documents have moved the needle with respect to Sponsor and CRO organization obligations in terms of how they plan and execute studies as well as prepare study assets for inclusion in a regulatory submission. But it is not just the US FDA when it comes to including data in a submission; Japan's PMDA has moved beyond the pilot phase into the voluntary phase with an eye on requiring submissions based on their endorsed data standards in 2020.

This highly interactive seminar will review each asset, its role in the submission and the impact that these final guidance documents have on how the asset is handled as it weaves its way through the drug development lifecycle on its way to regulators. Simultaneously we will review the similarities and key differences executing these same tasks when interacting with Japan's PMDA. A portion of the seminar will be dedicated to a discussion of "hot off the press" topics, including a review of FDA & PMDA behavior since these documents have been finalized including Sponsor feedback during the review period. We will also explore how other global regulatory bodies are embracing standards, with a focus on Canada, Europe and China.

Audience Level: Beginner to Intermediate - individuals who are new to the Pharmaceutical industry would benefit greatly for the opportunity to put their hard work creating analysis datasets and TLFs into the context of a regulatory submission. Conversely, experienced professionals who have created submission assets in the past who are looking for a refresher on recent changes to FDA & PMDA requirements, CDISC standards and the outlook on submission data requirements for other global regulatory bodies would also enjoy this seminar.
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Use Cases for Value-Level Metadata
Sandra Minjoe, Mario Widel
Thursday, June 3, 2021, 10:00 AM - 2:30 PM ET

Value-level metadata is nothing mysterious. It is simply a way to describe a variable when it differs based on some circumstances. When done properly, value-level metadata make a define.xml more reviewer-friendly.

A common use in ADaM for value-level metadata is when AVAL is derived based on PARAM, but this is not the only time to use value-level metadata. This seminar includes examples and exercises of value-level metadata in SDTM Findings, in ADaM BDS, for integration, to meet BIMO needs, and more.

Additionally, this seminar will provide guidance to help attendees decide when to use value-level metadata in order to help provide a clear message for anyone who will be using the data and metadata.
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Python Programing Seminar – Basic for Statistical Programmers and Statisticians
Kevin Lee
Friday, June 4, 2021, 10:00 AM - 2:30 PM ET

Python is one of the most popular language nowadays. Python can be used to build just about anything, and it is a great language for back-end web development, data analysis, scientific computing, machine learning and many more.

The seminar is intended for Statistical Programmers and Statisticians who are familiar with SAS programming. It is not easy for programmers and biostatisticians to learn new language alone. The seminar will provide basic concept and foundation of Python programming, and the seminar will provide its comparison and similarity with SAS programming. Therefore, Statistical Programmers and Statisticians have easier time to understand how Python programming works.

The basic Python Programming seminar will cover basic Python programming. It is recommended for those who has a little or no experience in Python programming. It will help SAS programmers and statisticians start Python programming and learn how to use Jupyter Notebook/Lab (the most popular python platform).

  • Introduction to Python for statistical programmers and statisticians
  • Jupyter Notebook (Python programming platform) download and implementation
  • Python Variables Type: Number, String, Lists, Dictionaries, Arrays, Data Frames
  • Simple variable manipulation - If & For statements
  • Python Function development and comparison with SAS Macro
  • Import external Modules/Functions
  • Reading and writing external data (excel, SAS datasets, Images)
  • Data manipulation using Python
  • Introduction of NumPy and Array
  • Introduction of Pandas and DataFrame: DataFrame vs SAS datasets
  • Basic data manipulation – merge, sort, variables drop/addition
  • Create SDTM DM dataset using SAS raw datasets

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    Instructor Biographies

    David Izard

    Dave Izard frequently finds himself at the intersection of clinical data standards, regulatory expectations and sponsor organization needs and desires. A pharmaceutical professional since 1997, he currently serves as Programming Director at GlaxoSmithKline, supporting Infectious Disease clinical asset development and GSK’s efforts to expand their regulatory submission capabilities. Earlier opportunities include serving as Senior Director of Clinical Data Standards at Chiltern (Covance), Clinical Data Consulting Lead at Accenture, Head of Octagon Research Solutions' SDTM practice, and a variety of Clinical Programming leadership roles at both GSK and Shire.

    He has served as a paper author & presenter, seminar instructor and section chair at industry conferences including the PharmaSUG main conference and Single Day Events, Pharmaceutical Users Software Exchange (PhUSE) Single Day Events, the Society of Clinical Data Management (SCDM) and various local and regional SAS meeting. Past PhUSE efforts include supporting the development of the Study Data Standardization Plan and Legacy Data Conversion Plan & Report templates. He holds Bachelors and Masters of Science Degrees in Computer Science from Bucknell and West Chester University respectively.

    Kevin Lee

    Kevin Lee is Data Scientist, statistician, Machine Learning working group lead, corporate/university trainer and evangelist in new technology.  Kevin supports Pharmaceutical industry as AVP of AI/Machine Learning Consultant at Genpact.  Among all the therapeutic area, Kevin always loves oncology studies, and he is an active supporter on oncology-specific standards such as CDISC Tumor datasets, control terminology and response criteria on each study type.  Kevin wants to innovate pharmaceutical industry with AI/Machine Learning technology, and he currently leads AI/Machine Learning working group in PhUSE.  He also teaches Machine Learning and Python programming in University and corporations.  Kevin has presented about 100 papers at the various conferences including many oncology-related and Machine Learning based papers.  Kevin earned an M.S. in Applied Statistics at Villanova University following a B.S. from University of Pennsylvania.   Kevin is a life-time learner who loves to learn and share.  

    Sandra Minjoe

    Sandra Minjoe started programming in the pharma/biotech industry in 1993. She is a Senior Principal Clinical Data Standards Consultant at PRA Health Sciences. Sandra is the former CDISC ADaM Team Lead. She’s been part of the ADaM team since 2001, proposed structures that became ADSL and OCCDS, and continues to work on sub-teams. Her focus is the fundamental principle of traceability. In addition to her CDISC involvement, Sandra is an emeritus PharmaSUG Executive Committee member.

    Mario Widel

    Mario Widel is a statistical programmer at Reata Pharmaceuticals. He has been involved in CDISC related activities since 2007. In his current role, Mario focuses on process development for submission data and documentation. He is a member of the ADaM team, a CDISC authorized SDTM and ADaM instructor and has presented at numerous conferences including PharmaSUG, JSM, SAS Global Forum and PhUSE